ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025
Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- CT-based deep learning signatures associated with transcriptomic heterogeneity and combined with nutritional biomarkers improve prediction of 3-year overall survival in esophageal squamous cell carcinoma.Insights into imaging · 2026Article
- Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma.Frontiers in oncology · 2026Article
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16 authors.
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Abstract
backgroundThe present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients.
methodsA total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed.
resultsA total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767-0.900) for the training cohort and 0.733 (95% CI, 0.574-0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761-0.899) in the training cohort and 0.793 (95% CI, 0.653-0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795-0.928) in the training cohort and 0. 837 (95% CI, 0.705-0.969) in the test cohort.
conclusionAn integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC.
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